Database, Data Mining, and Data Warehousing Techniques: A Comprehensive Overview

This paper explores the concepts of database, data mining, and data warehousing, highlighting their significance in modern organizations. It delves into the processes, techniques, and applications of these technologies, providing a comprehensive overview of their role in managing and extracting valuable insights from data. The paper examines the importance of data mining in various sectors, including banking, retail, healthcare, and manufacturing, showcasing its ability to enhance business decision-making and drive organizational growth. It also discusses the benefits of data warehousing in centralizing and managing large volumes of data, enabling organizations to gain a holistic view of their operations and make informed strategic decisions.

ITC540
IT Infrastructure Management
Assessment 3
Case study and IT Research
Student Name: Abhishek Aswal
Student ID: 11614903
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Contents
Task A..............................................................................................................................................3
Task B..............................................................................................................................................6
Task C............................................................................................................................................11
References......................................................................................................................................12
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Task A
After briefly analyzing the case study of
AstraZeneca Terminates $1.4B Outsourcing
Contract with IBM several raised questions
can be answered examining the case study
which is provided below:
1. What mistakes did AstraZeneca
make?
AstraZeneca is world’s one of the
biopharmaceutical leading companies that
emphasize on development,
commercialization, and discovery of many
prescribed medicine for nearly six areas of
health care. AstraZeneca made huge
mistakes which are described below.
 The master service agreement (MSA)
contains 90 clauses and 32 schedules
that govern the condition of services of
an IT infrastructure.
 It was completely imprecise in case of
dealing with some exit obligations on the
IBM along with termination of MSA.
 AstraZeneca used specifications based
on the outcome of the deal with IBM
that has to lead to the failure of that
particular contract. Though in that
contract these specifications were
planned to use so as to encourage the
innovations among several vendors and
even the contracts were estimated to be a
model which is ground-breaking but
later this model results into failure
because the business of AstraZeneca was
rapidly changing and the designed
contract didn’t include such pace.
2. What mistakes did IBM make?
The mistakes made by IBM are listed as:
 IBM provided certain ‘shared services’
for nearly 12 months. These are the
services that are provided by IBM to
AstraZeneca utilizing infrastructures
can’t be provided to any other suppliers
even not handed to AstraZeneca.
 IBM proposed the provision for the
termination services that was later
rejected by the court.
 IBM proposed the provision for fixed
fees after receiving the plan for IT
transfer that was also later rejected by
the court.
3. Why are outsourcing contracts for
five or more years?
These outsourcing contracts are generally
for 5 or more than 5 years or are generally
long-term or very large as they are difficult
enough to change or modify because of
these contracts vendors accrues or gather
maximum profit from the made deal. These
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big outsource deals enable vendors to make
high investments within initial 2 years as
any services used to get customized and set
up and in later 2 or 3 years vendors used to
expect a maximum profit that the reason
large or outsourcing deals are of 5 or more
years.
4. Why do you think two major
corporations could make such
mistakes?
These mistakes occur when two major
companies face discrepancy when they don’t
agree on following things:
 What must be constituted as ’shared
infrastructure’ for both companies?
 What can be the exact length established
for a period of termination assistance?
 Whether the provision of fixed fees is
conditional after receiving the plan for
IT transfer.
5. Do you think the 2007 SLA was
doomed to fail? Explain your answer.
Yes, I think SLA of 2007 was doomed to get
failed because of these reasons:
 The SLA extensively contains 90 clauses
along with 32 schedules that govern the
condition of services of an IT
infrastructure in nearly 60 countries.
 It was completely imprecise in case of
dealing with some exit obligations on the
IBM if the termination of a contract
takes place.
 AstraZeneca completely depends on its
several IT capabilities along with its
R&D because for it both are important.
 AstraZeneca used specifications based
on the outcome of the deal with IBM
that has to lead to the failure of SLA as
AstraZeneca was rapidly changing and
the designed contract didn’t include such
pace.
6. What provisions in the 2001 SLAs
protect AstraZeneca and the vendors?
The provisions SLAs made in 2001 to
prevent vendors and AstraZeneca are listed
below:
 Designing of SLAs is done for the
protection of vendors, AstraZeneca, and
service provider but not of customers
until and unless customers actively play
an informed role in parameters and
provisions.
 SLAs prevent both parties from making
them aware regarding their roles and
responsibilities and when they must be
held liable for weakening to live up to
their responsibilities.
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 An SLA which is strong can prevent
many dangers and disruptions that arise
while migrating or sourcing it towards
the cloud.
 The parameters and provisions of several
deals are companies’ only protections in
the case when different terms are not
fulfilled even after the termination of
arrangement takes place.
 SLAs don’t allow vendors and
AstraZeneca to sign any of the contracts
or deal without having a thorough and
detailed legal review.
 SLA doesn’t have any template and
solution for each cloud of the vendor is
completely unique.
 If any SLAs vendor is light with its
details than it can alone proves to be an
indicator determining light
accountability of vendor.
 If any vendor refuses cloud migration or
sourcing for SLAs improvement or vital
point negotiation, then that particular
vendor is not considered.
7. Why would parties prefer to use an
arbitrator instead of filing a lawsuit in
court?
The parties generally prefer to utilize
arbitrator instead of lawsuits because
 The main point is to develop a strong
and positive relationship with the vendor
at the time of selection of vendors. If any
company selects the wrong vendor who
gets entered into a service deal or
contract then chances are that the
software, its app or its implementation
will most likely fail and no vendor could
resolve this occurred problems. Failures
mainly occur by lawsuits.
 Use of arbitrator leads to stability
whereas on the other side lawsuits create
instability.
 To reduce technical or interpersonal
conflicts with several IT vendors, some
businesses require thorough vendor
research.
 It is crucial to put several questions
related to product and services vendor
will grant and receive many specifics
(Turban & Volonino & Wood, 2015).
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Task B
The topic I have chosen is Database, Data
Mining, and Data Warehouse
Abstract
The database is the set of information
gathered which is organized in such a way
that it can be managed, accessed and easily
updated. In the database, the data is
managed in columns, rows, and tabular
format. Data mining is one of the crucial
parts of management of database system so
that the information flow in the system is
organized. On daily basis, a lot of data used
to get a transfer from the industry of
information and to provide this data flow a
structure it requires data mining. Data
mining helps to extract useful data from the
provided information. To use the data
efficiently many large or small organizations
adopts the procedure of data mining. Data
warehousing is defined as the centralized
data in many large organizations. The report
comprises of several techniques of data
mining and data warehousing, its
applications in industries along with its
importance. It describes the overview of
processes like data mining and data
warehousing for managing the flow of data
in any organization.
Index terms
This project index terms are defines as
Database, Data mining, Data warehousing,
techniques of data mining and data
warehousing and applications of Data
mining and Data warehousing. Some key
terms are introduced which describes the
process for data mining. I have chosen this
topic to examine the IT-based infrastructure
of several trending organizations.
Introduction
An organization growth can be best defined
by several techniques of database and data
mining. The business process can be
improved by properly utilizing the
information provided effectively. The
process is initiated by collecting several data
from the created database and then data
mining of gathered data takes place. By
appropriate planning the extraction of
information as useful data takes place. The
data stored is further quantified and
analyzed depending on organizational
requirement (Elmasri, 2008).
Subtopics and Supporting Arguments
Database
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The database is a set of information or data
which is organized for quick searching and
computer retrieval. The database is the set of
information gathered which is organized in
such a way that it can be managed, accessed
and easily updated. In the database, the data
is managed in columns, rows, and tabular
format. While the addition of new data or
information in a database the previous data
gets expanded, deleted and updated. The
database contains data record aggregations
like transaction of sales, catalogs of products
and profiles of the customer (Date, 2006).
Data mining
The process of data mining proves to be
effective so as to determine the investment
returns to identify the cost of development
of the required project. This process initiates
many organizational based business
opportunities. This helps several companies
to promote and encourage its business not
only among domestic consumers or users
but also spread it to international consumers
(Han, Pei & Kamber, 2011). The
terminology of data mining can be used to
analyze the usefulness of data and identifies
organizational weakness along with
providing solutions related to business. The
solutions for business contains the better
development of planning of IT strategies,
using services for advertisement, and to
enhance organizational based operations for
the business. The extracted data from the
process of data mining helps in making
crucial business-related decisions by using
several policies of marketing (Mining,
2006).
Data Warehousing
It is referred as the centralized data in
several organizations dealing with large
business defines data warehousing. Here the
data is defined as centralized as it is
accessible from a single location to various
parts in an organization. Like data or
information transferring from headquarters
of a company to local branches of the
company. The collection of warehouse data
is a non-volatile form of data that includes
time variant and subject-oriented
information delivery over several channels
of organization used for communication.
This process of warehousing of data helps in
the organizational management so as to
make some good decisions to enhance their
business values (Mallach, 2000).
Applications of Database
The database is very crucial and it is used in
many organizations as it is used to store data
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and several applications that are listed below
that are:
 Banking: While making daily
transactions in banks this can be done
just by sitting at home and sending
money. This is only possible when all
details are properly recorded in the
database of all customers.
 Railway reservation system: For
reservations in railways database is
required for maintaining proper ticket
booking records along with details of
arrival and departure time of the train.
Also, the database gets updated regularly
so that people get notified regarding the
delays in trains.
 Universities and colleges: Several
examinations are conducted online in
colleges and universities so all students
related details need to be properly
maintained and managed using proper
database (Hui, Jiang, Li & Zhou, 2009).
 Library management system: A library
has books in thousand number making it
difficult for a librarian to remember
them all so a proper database is used to
be created that contains all details of
available books along with its location in
the library. The database even maintains
the record regarding the book issued and
its return date.
 Online shopping: This has become a
trend these days so the products that are
added and later sold or purchased
completely works on database system as
it requires proper management
(Bestavros, Lin & Son, (Eds.) 2012).
Applications of Data Mining
The Data Mining is very important as it is
used in many organizations and several
application of it is listed below:
 Communication: The major source to
know the reviews of the customer
regarding the products of a particular
brand in the available market can be best
grabbed in a way of interaction with
several customers so as to receive
informative data from it. Skills of a
business analyst can prove to be very
useful in this particular case. Data
mining analyses company’s financial
statistics of profit and benefits. Different
telecommunication and multimedia
industries are using data mining
techniques for better understanding of
customer needs and to maintain an
image in the growing and competitive
market.
 Retail: For market retailers, the data
mining techniques are really necessary
so as to enhance the consumer
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relationship that results in better offers
and effective deals both reliable for
customers as well as retailers. The main
strategy is to reduce product price
without any quality compromise.
Retailers are provided with several data
models which are effective and can be
adopted by retailers to simplify their
business and reach their targeted
customers (Lior, 2014).
 Manufacturing: The supply chains of
different manufacturing companies
needs to be updated regarding its
services and products. The process of
data mining enables manufacturers to
analyze customer requirements and
examine its IT assets from market recent
statistics. It even manages large data
related to suppliers, clients, and retailers
which are maintained regularly.
 Insurance: Insurance based companies
contains large data in its database that
includes applications of clients, new
account details, account change and
other related details. The data mining
helps insurance companies to prevent
themselves from cyber vulnerabilities.
Data mining secures the stored data from
different frauds by providing strategies
for risk management and risk analysis
(Koh & Tan, 2011).
Applications of Data Warehousing
The Data Warehousing is very important as
it is used in many organizations and several
application of it is listed below:
 Healthcare: It is an important sector
that uses data warehousing. Healthcare
has all details of its clinical, financial
and employee based record which are
fed to several warehouses because it
provides them with different strategies,
calculates outcomes, analyses and tracks
the feedback of their services, develop
reports of patients, data sharing and
providing better services for medical aid.
 Services sectors: Data warehouses are
very beneficial in case of providing
services to maintenance sector regarding
its financial records, profiling of
customer, management of resources,
revenue patterns and human resources.
 Transportation industry: Data
warehouses used to record data of
customers that allows experimenting
traders with the targets of the market and
designing several campaigns of
marketing keeping requirements of
customers in mind.
 Government purpose: The government
utilizes several data warehouses so as to
maintain records of tax and analyze them
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well, analyze records of health policy
and its providers. Also, data warehouses
maintain a complete database of criminal
law (Chau, Cao, Anson, & Zhang,
2003).
Tools and techniques for Data Mining
The principles of data mining are utilized to
calculate the big data efficiency in the
system of the database. So for describing it
well, some data mining techniques are
defines that are:
 Classification: Data classification is
performed depending on objects which
are used for collection of information in
a database system. This technique helps
in data management according to groups
like group associated with several social
entities and other groups that are
considered as a single entity.
 Association: This technique of data
mining describes correlation among
various entities of several database
items. The software tool named as
‘InfoSphere Warehouse’ is utilized in
the database for information flow
detection which helps in better
understanding of relationships between
several items like customers and
products.
 Clustering: It enables the user to
categorize among provided attributes of
any particular item so as to arrange them
in defined sequence depending upon its
requirements. Clustering defines a
structure of data which is managed by
the system of the database. To any non-
volatile flow of information, clustering is
an approach which is reasonable (Phyu,
2009).
Conclusion
Data mining is something much more than
just running several data queries which are
complex and stored in a particular database.
The database must work with available data,
restructure or reformat it regardless of one
using SQL, or database based on documents
like simple flat files or Hadoop.
Identification of requirement of information
format depends completely upon the
analysis of technique required to be
performed. These different techniques are
applied after having the complete format of
information required regardless of data set
or structure underlying need.
The paper above explains the data mining
importance in several sectors. It even
describes database, data mining and data
warehouse usefulness and applications in
several sectors which help in making better
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decisions regarding the effective growth of
the business of any organization.
Task C
Analysis of Turnitin Report
Depending on Turnitin report similarity
index some set of questions are answered
below.
a). Are any of the bold, colored text
matches in my self-check report missing
in-text references?
No, text segments like this are present in the
report.
b). Do any of the bold, colored text
matches in my self-check report include
more than three words in a row copied
from the original source without
quotation marks?
No colored matches are found in a document
of Turnitin that are not quoted. The data
gathered by means of internet is referenced
properly and is marked or demonstrated as a
quote provided in the report of Turnitin.
c). Do direct quotations take up more
than 10% of the essay?
The chosen topic for the essay is “Database,
Data Mining, Data Warehouse” which is
completely written by me in my own
language and not copied from any source of
internet.
d). Are any of the bold, colored text
matches in my originality report purely
coincidental?
The document doesn’t contain any of such
matches. The text or data extracted or
gathered from the internet must be
referenced appropriately.
e). Do any of the short strings of matching
text indicate that my attempts at
paraphrasing were not completely
successful?
The Turnitin report, very well clarifies that I
haven’t chosen any text directly from
internet sources. The information I have
gathered and collected was paraphrased in
my language after properly understanding it.
f). Have I synthesized all of the sources’
ideas into my essay by introducing each
piece of source information with a signal
phrase and by adding my own comments
or interpretation to it in the following
sentence?
Based on my knowledge and learning’s I
have written this essay in which I have
demonstrated my thoughts related to
database, data mining and data warehouse
with respect to technology type.
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References
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(2015). Information Technology for
Management: Digital strategies for
Insight, Action, and Sustainable
Performance, 10th edition. USA: John
Wiley and Sons.
 Date, C. J. (2006). An introduction to
database systems. Pearson Education
India. Retrieved from http://db.inf.uni-
tuebingen.de/staticfiles/teaching/ss10/db
s1/02_er.pdf
 Elmasri, R. (2008). Fundamentals of
database systems. Pearson Education
India. Retrieved from
http://users.cs.uoi.gr/~mdrosou/docs/thes
is.pdf
 Han, J., Pei, J., & Kamber, M.
(2011). Data mining: concepts and
techniques. Elsevier. Retrieved from
http://www7.inra.fr/bbt/2003/mars03/BB
T03Pap.pdf
 Mining, W. I. D. (2006). Data Mining:
Concepts and Techniques. Morgan
Kaufinann. Retrieved from
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dm2007/05_dbdm2007_Data
%20Mining.pdf
 Mallach, E. (2000). Decision support
and data warehouse systems.
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cision_Support_and_Data_Warehouse_S
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562fb88508ae02b5739a1c12.pdf
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(Eds.). (2012). Real-time database
systems: Issues and applications (Vol.
396). Springer Science & Business
Media. Retrieved from
http://www.cs.bu.edu/techreports/1996-
013-rtdb96-report.ps.Z
 Hui, M., Jiang, D., Li, G., & Zhou, Y.
(2009, March). Supporting database
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Engineering, 2009. ICDE'09. IEEE 25th
International Conference on (pp. 832-
843). IEEE. Retrieved from
http://dbease.cs.tsinghua.edu.cn/ligl/pape
rs/icde09-mstore.pdf
 Lior, R. (2014). Data mining with
decision trees: theory and
applications (Vol. 81). World scientific.
Retrieved from https://eric.univ-
lyon2.fr/~ricco/tanagra/fichiers/fr_Tanag
ra_DM_with_Decision_Trees.pdf
 Koh, H. C., & Tan, G. (2011). Data
mining applications in
healthcare. Journal of healthcare
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